# Importing Necessary Libraries
import cv2
import os
import shutil
import math
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# import shutil
# # Clear the existing mount point
# shutil.rmtree('/content/drive')
# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-3-de7c54a020a6> in <cell line: 8>() 6 # Mount Google Drive 7 from google.colab import drive ----> 8 drive.mount('/content/drive') /usr/local/lib/python3.10/dist-packages/google/colab/drive.py in mount(mountpoint, force_remount, timeout_ms, readonly) 98 def mount(mountpoint, force_remount=False, timeout_ms=120000, readonly=False): 99 """Mount your Google Drive at the specified mountpoint path.""" --> 100 return _mount( 101 mountpoint, 102 force_remount=force_remount, /usr/local/lib/python3.10/dist-packages/google/colab/drive.py in _mount(mountpoint, force_remount, timeout_ms, ephemeral, readonly) 281 'https://research.google.com/colaboratory/faq.html#drive-timeout' 282 ) --> 283 raise ValueError('mount failed' + extra_reason) 284 elif case == 4: 285 # Terminate the DriveFS binary before killing bash. ValueError: mount failed
# Function for Formatting Dataset
def FormatDataset(dataset_src, dataset_dest, classes):
# Making a Copy of Dataset
new_cropped_dest = [os.path.join(dataset_dest, cls, 'CROPPED') for cls in classes]
new_complete_dest = [os.path.join(dataset_dest, cls, 'COMPLETE') for cls in classes]
cropped_src = [ dataset_src + "/im_" + cls + "/im_" + cls + "/CROPPED" for cls in classes ]
complete_src = [ dataset_src + "/im_" + cls + "/im_" + cls for cls in classes ]
for (dest1, dest2) in zip(new_cropped_dest, new_complete_dest):
os.makedirs(dest1, exist_ok=True)
os.makedirs(dest2, exist_ok=True)
# Formating Cropped Images
for (src, new_dest) in zip(cropped_src, new_cropped_dest):
for file in os.listdir(src):
filename, file_ext = os.path.splitext(file)
if file_ext == '.bmp':
img_des = os.path.join(new_dest, filename + '.jpg')
img = cv2.imread(os.path.join(src, file))
img = cv2.resize(img, (64, 64))
img = cv2.copyMakeBorder(img, 1, 1, 1, 1, cv2.BORDER_CONSTANT, value=0)
img = cv2.blur(img, (2, 2))
cv2.imwrite(img_des ,img)
# Formatting Complete Images
for (src, new_dest) in zip(complete_src, new_complete_dest):
for file in os.listdir(src):
filename, file_ext = os.path.splitext(file)
if file_ext == '.bmp':
img_des = os.path.join(new_dest, filename + '.jpg')
img = cv2.imread(os.path.join(src, file))
img = cv2.resize(img, (256, 256))
img = cv2.copyMakeBorder(img, 2, 2, 2, 2, cv2.BORDER_CONSTANT, value=0)
img = cv2.blur(img, (2, 2))
cv2.imwrite(img_des, img)
# Source Location for Dataset
src = '/content/drive/Shareddrives/Computer Vision Final Project'
# Destination Location for Dataset
dest = '/content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer'
# Image Classes
classes = ["Dyskeratotic", "Koilocytotic", "Metaplastic", "Parabasal", "Superficial-Intermediate"]
# Formatting Dataset
FormatDataset(src, dest, classes)
import os
import matplotlib.pyplot as plt
root_dir = "/content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer"
classes = ["Dyskeratotic","Koilocytotic","Metaplastic","Parabasal","Superficial-Intermediate"]
def GetDatasetSize(path, classes, main="CROPPED"):
num_of_image = {}
total_images = 0
for cls in classes:
# Counting the Number of Files in the Folder
num_files = len(os.listdir(os.path.join(path, cls, main)))
num_of_image[cls] = num_files
total_images += num_files
return num_of_image, total_images
def plot_class_distribution(class_image_counts):
classes = list(class_image_counts.keys())
counts = list(class_image_counts.values())
colors = ['orange' if cls in ["Dyskeratotic", "Koilocytotic"] else
'yellow' if cls == "Metaplastic" else
'green' for cls in classes]
plt.figure(figsize=(10, 6))
plt.bar(classes, counts, color=colors)
plt.xlabel('Class')
plt.ylabel('Number of Images')
plt.title('Number of Images per Class')
plt.xticks(rotation=45)
plt.show()
class_image_counts, total_images = GetDatasetSize(root_dir, classes, "COMPLETE")
print("COMPLETE")
print("Number of images per class:", class_image_counts)
print("Total number of images:", total_images)
# Plot the distribution
plot_class_distribution(class_image_counts)
COMPLETE
Number of images per class: {'Dyskeratotic': 223, 'Koilocytotic': 238, 'Metaplastic': 271, 'Parabasal': 108, 'Superficial-Intermediate': 126}
Total number of images: 966
class_image_counts, total_images = GetDatasetSize(root_dir, classes, "CROPPED")
print("CROPPED")
print("Number of images per class:", class_image_counts)
print("Total number of images:", total_images)
# Plot the distribution
plot_class_distribution(class_image_counts)
CROPPED
Number of images per class: {'Dyskeratotic': 813, 'Koilocytotic': 825, 'Metaplastic': 793, 'Parabasal': 787, 'Superficial-Intermediate': 831}
Total number of images: 4049
import os
import cv2
import matplotlib.pyplot as plt
def display_images(path, classes, main="CROPPED", num_images=5):
fig, axes = plt.subplots(len(classes), num_images, figsize=(15, 15))
color_map = {
"Dyskeratotic": "orange",
"Koilocytotic": "orange",
"Metaplastic": "yellow",
"Parabasal": "green",
"Superficial-Intermediate": "green"
}
for i, cls in enumerate(classes):
cls_path = os.path.join(path, cls, main) # Construct the path to the directory containing the images for the current class.
images = os.listdir(cls_path)[:num_images]
for j, image_file in enumerate(images):
img_path = os.path.join(cls_path, image_file)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert the image from BGR (OpenCV default) to RGB (matplotlib default).
axes[i, j].imshow(img) # Display the image in the appropriate subplot.
axes[i, j].axis('off')
if j == 0: # Set the title of the first image in each row to the class name.
axes[i, j].set_title(cls, color='black', bbox=dict(facecolor=color_map[cls], edgecolor='none', pad=5))
plt.tight_layout()
plt.show()
print("COMPLETE")
display_images(root_dir, classes, "COMPLETE", 5)
COMPLETE
print("CROPPED")
display_images(root_dir, classes, "CROPPED", 5)
CROPPED
import os
import shutil
import numpy as np
# Function for Creating Train / Validation / Test folders (One time use Only)
def TrainValTestSplit(root_dir, classes_dir, main="CROPPED", val_ratio=0.15, test_ratio=0.15):
for cls in classes_dir:
# Creating Split Folders inside the root_dir
# For each class, create directories for training, validation, and test sets inside root_dir.
os.makedirs(os.path.join(root_dir, 'train', cls), exist_ok=True)
os.makedirs(os.path.join(root_dir, 'val', cls), exist_ok=True)
os.makedirs(os.path.join(root_dir, 'test', cls), exist_ok=True)
# Folder to copy images from
src = os.path.join(root_dir, cls, main)
# Splitting the Files in the Given ratio
# List all file names in the source directory.
allFileNames = os.listdir(src)
# Shuffle the file names to randomize the order.
np.random.shuffle(allFileNames)
# Split the file names into training, validation, and testing sets based on the specified ratios.
train_FileNames, val_FileNames, test_FileNames = np.split(
np.array(allFileNames),
[int(len(allFileNames) * (1 - (val_ratio + test_ratio))), int(len(allFileNames) * (1 - test_ratio))]
)
# Convert the file names into full file paths for training, validation, and testing sets.
train_FileNames = [os.path.join(src, name) for name in train_FileNames.tolist()]
val_FileNames = [os.path.join(src, name) for name in val_FileNames.tolist()]
test_FileNames = [os.path.join(src, name) for name in test_FileNames.tolist()]
# Printing the Split Details
# Print the number of total images, training images, validation images, and testing images for each class.
print(cls, ':')
print('Total images: ', len(allFileNames))
print('Training: ', len(train_FileNames))
print('Validation: ', len(val_FileNames))
print('Testing: ', len(test_FileNames))
# Copy-pasting images to respective directories
# Copy each image to its respective directory (train, val, or test) based on the split.
for name in train_FileNames:
shutil.copy(name, os.path.join(root_dir, 'train', cls))
for name in val_FileNames:
shutil.copy(name, os.path.join(root_dir, 'val', cls))
for name in test_FileNames:
shutil.copy(name, os.path.join(root_dir, 'test', cls))
print()
# Performing Train / Validation / Test Split
root_dir = "/content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer"
classes = ["Dyskeratotic", "Koilocytotic", "Metaplastic", "Parabasal", "Superficial-Intermediate"]
TrainValTestSplit(root_dir, classes)
Dyskeratotic : Total images: 813 Training: 569 Validation: 122 Testing: 122 Koilocytotic : Total images: 825 Training: 577 Validation: 124 Testing: 124 Metaplastic : Total images: 793 Training: 555 Validation: 119 Testing: 119 Parabasal : Total images: 787 Training: 550 Validation: 118 Testing: 119 Superficial-Intermediate : Total images: 831 Training: 581 Validation: 125 Testing: 125
import os
# Function to count number of images in each class directory for train, val, and test
def count_images_in_split_dirs(root_dir, classes):
splits = ['train', 'val', 'test']
counts = {split: {cls: 0 for cls in classes} for split in splits}
for split in splits:
for cls in classes:
class_dir = os.path.join(root_dir, split, cls)
if os.path.exists(class_dir):
counts[split][cls] = len(os.listdir(class_dir))
else:
counts[split][cls] = 0
return counts
# Define the root directory and classes
root_dir = "/content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer"
classes = ["Dyskeratotic", "Koilocytotic", "Metaplastic", "Parabasal", "Superficial-Intermediate"]
# Get the counts of images
image_counts = count_images_in_split_dirs(root_dir, classes)
# Print the counts
for split in image_counts:
print(f"\n{split.upper()}:")
for cls in image_counts[split]:
print(f" {cls}: {image_counts[split][cls]} images")
TRAIN: Dyskeratotic: 569 images Koilocytotic: 577 images Metaplastic: 555 images Parabasal: 550 images Superficial-Intermediate: 581 images VAL: Dyskeratotic: 122 images Koilocytotic: 124 images Metaplastic: 119 images Parabasal: 118 images Superficial-Intermediate: 125 images TEST: Dyskeratotic: 122 images Koilocytotic: 124 images Metaplastic: 119 images Parabasal: 119 images Superficial-Intermediate: 125 images
# Importing Keras for Image Classification
import keras
from keras.layers import Dense,Conv2D, Flatten, MaxPool2D, Dropout
from keras.models import Sequential
from keras.preprocessing import image
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
# CNN Model
model = Sequential()
# Convolutional Layer with input shape (64,64,3)
model.add(Conv2D(filters=16, kernel_size= (3,3), activation= 'relu', input_shape=(64,64,3)) )
model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu' ))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu' ))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu' ))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(rate=0.25))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(Dropout(rate=0.25))
model.add(Dense(units=5, activation='sigmoid'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'] )
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 62, 62, 16) 448
conv2d_1 (Conv2D) (None, 60, 60, 32) 4640
max_pooling2d (MaxPooling2 (None, 30, 30, 32) 0
D)
conv2d_2 (Conv2D) (None, 28, 28, 64) 18496
max_pooling2d_1 (MaxPoolin (None, 14, 14, 64) 0
g2D)
conv2d_3 (Conv2D) (None, 12, 12, 128) 73856
max_pooling2d_2 (MaxPoolin (None, 6, 6, 128) 0
g2D)
dropout (Dropout) (None, 6, 6, 128) 0
flatten (Flatten) (None, 4608) 0
dense (Dense) (None, 64) 294976
dropout_1 (Dropout) (None, 64) 0
dense_1 (Dense) (None, 5) 325
=================================================================
Total params: 392741 (1.50 MB)
Trainable params: 392741 (1.50 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
# Expand the size of dataset with new transformed images from the original dataset using ImageDataGenerator.
train_datagen = image.ImageDataGenerator(zoom_range = 0.2, shear_range = 0.2 , rescale = 1./255 , horizontal_flip=True)
val_datagen = image.ImageDataGenerator(rescale = 1./255)
test_datagen = image.ImageDataGenerator(rescale = 1./255)
# The train_data object is an instance of a Keras DirectoryIterator, which generates batches of data from the specified directory.
# The flow_from_directory method reads images from the specified directory and applies the transformations defined in the train_datagen object (such as augmentation and normalization).
# Resizes Images: All images are resized to 64x64 pixels.
# Batch Processing: Images are processed and yielded in batches of 100.
# Categorical Labels: The labels for the images are one-hot encoded.
# The flow_from_directory function is a powerful way to generate batches of tensor image data with real-time data augmentation. It is especially useful when having a large dataset organized into subdirectories by class. The function reads the images, applies the specified preprocessing steps, and yields them in batches for training the neural network.
train_data = train_datagen.flow_from_directory(directory= root_dir + "/train", target_size=(64, 64), batch_size=100, class_mode = 'categorical')
Found 2832 images belonging to 5 classes.
train_data.class_indices
{'Dyskeratotic': 0,
'Koilocytotic': 1,
'Metaplastic': 2,
'Parabasal': 3,
'Superficial-Intermediate': 4}
val_data = val_datagen.flow_from_directory(directory= root_dir + "/val", target_size=(64, 64), batch_size=100, class_mode = 'categorical')
Found 608 images belonging to 5 classes.
test_data = test_datagen.flow_from_directory(directory= root_dir + "/test", target_size=(64, 64), batch_size=100, class_mode = 'categorical')
Found 609 images belonging to 5 classes.
# Adding Model check point Callback
# This callback is used during the training process to save the model weights. It monitors the validation accuracy and saves the model only if there is an improvement.
from tensorflow.keras.callbacks import ModelCheckpoint
# Define the full filepath for saving the best model
filepath = os.path.join(root_dir, "cervical_cancer_best_model.hdf5")
# Adding Model Checkpoint Callback
mc = ModelCheckpoint(
filepath=filepath,
monitor='val_accuracy',
verbose=1, # When set to 1, the callback will print messages when the model is being saved.
save_best_only=True, # When set to True, the callback saves the model only when the monitored metric (val_accuracy) improves. This ensures that only the best model, in terms of validation accuracy, is saved.
mode='auto'
)
call_back = [mc]
# Fitting the Model
# steps_per_epoch = 28 (how many batches on one epoch)
# This parameter defines the number of batches of samples to be used in each epoch.
# Essentially, it is the number of times the model will be updated in one epoch.
# Since the batch size is 100, then 28 steps per epoch mean that the model will see 2800 (28 * 100) samples in one epoch.
# validation_steps=6
# This parameter defines the number of batches of samples to be used in each validation epoch.
# This means that in each epoch, the model will see 600 (6 * 100) samples from the validation set.
cnn = model.fit(train_data,
steps_per_epoch= 28,
epochs= 64,
validation_data= val_data,
validation_steps= 6,
callbacks = call_back )
Epoch 1/64 28/28 [==============================] - ETA: 0s - loss: 1.4633 - accuracy: 0.3034 Epoch 1: val_accuracy improved from -inf to 0.37333, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 2349s 84s/step - loss: 1.4633 - accuracy: 0.3034 - val_loss: 1.2985 - val_accuracy: 0.3733 Epoch 2/64 28/28 [==============================] - ETA: 0s - loss: 1.1777 - accuracy: 0.5190 Epoch 2: val_accuracy improved from 0.37333 to 0.65667, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 11s 383ms/step - loss: 1.1777 - accuracy: 0.5190 - val_loss: 0.9799 - val_accuracy: 0.6567 Epoch 3/64 28/28 [==============================] - ETA: 0s - loss: 1.0051 - accuracy: 0.6080 Epoch 3: val_accuracy improved from 0.65667 to 0.66000, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 415ms/step - loss: 1.0051 - accuracy: 0.6080 - val_loss: 0.9196 - val_accuracy: 0.6600 Epoch 4/64 28/28 [==============================] - ETA: 0s - loss: 0.9078 - accuracy: 0.6633 Epoch 4: val_accuracy improved from 0.66000 to 0.73167, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 11s 408ms/step - loss: 0.9078 - accuracy: 0.6633 - val_loss: 0.7619 - val_accuracy: 0.7317 Epoch 5/64 28/28 [==============================] - ETA: 0s - loss: 0.8248 - accuracy: 0.6903 Epoch 5: val_accuracy did not improve from 0.73167 28/28 [==============================] - 10s 370ms/step - loss: 0.8248 - accuracy: 0.6903 - val_loss: 0.7761 - val_accuracy: 0.6883 Epoch 6/64 28/28 [==============================] - ETA: 0s - loss: 0.7842 - accuracy: 0.7072 Epoch 6: val_accuracy did not improve from 0.73167 28/28 [==============================] - 11s 392ms/step - loss: 0.7842 - accuracy: 0.7072 - val_loss: 0.7119 - val_accuracy: 0.7317 Epoch 7/64 28/28 [==============================] - ETA: 0s - loss: 0.7546 - accuracy: 0.7204 Epoch 7: val_accuracy improved from 0.73167 to 0.74667, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 11s 409ms/step - loss: 0.7546 - accuracy: 0.7204 - val_loss: 0.6603 - val_accuracy: 0.7467 Epoch 8/64 28/28 [==============================] - ETA: 0s - loss: 0.6902 - accuracy: 0.7401 Epoch 8: val_accuracy improved from 0.74667 to 0.77000, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 11s 410ms/step - loss: 0.6902 - accuracy: 0.7401 - val_loss: 0.6111 - val_accuracy: 0.7700 Epoch 9/64 28/28 [==============================] - ETA: 0s - loss: 0.6603 - accuracy: 0.7643 Epoch 9: val_accuracy did not improve from 0.77000 28/28 [==============================] - 11s 382ms/step - loss: 0.6603 - accuracy: 0.7643 - val_loss: 0.5964 - val_accuracy: 0.7683 Epoch 10/64 28/28 [==============================] - ETA: 0s - loss: 0.6631 - accuracy: 0.7566 Epoch 10: val_accuracy did not improve from 0.77000 28/28 [==============================] - 10s 373ms/step - loss: 0.6631 - accuracy: 0.7566 - val_loss: 0.6260 - val_accuracy: 0.7633 Epoch 11/64 28/28 [==============================] - ETA: 0s - loss: 0.6443 - accuracy: 0.7694 Epoch 11: val_accuracy improved from 0.77000 to 0.77667, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 422ms/step - loss: 0.6443 - accuracy: 0.7694 - val_loss: 0.6339 - val_accuracy: 0.7767 Epoch 12/64 28/28 [==============================] - ETA: 0s - loss: 0.6400 - accuracy: 0.7629 Epoch 12: val_accuracy improved from 0.77667 to 0.80833, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 428ms/step - loss: 0.6400 - accuracy: 0.7629 - val_loss: 0.5055 - val_accuracy: 0.8083 Epoch 13/64 28/28 [==============================] - ETA: 0s - loss: 0.5383 - accuracy: 0.8042 Epoch 13: val_accuracy did not improve from 0.80833 28/28 [==============================] - 11s 386ms/step - loss: 0.5383 - accuracy: 0.8042 - val_loss: 0.5701 - val_accuracy: 0.8033 Epoch 14/64 28/28 [==============================] - ETA: 0s - loss: 0.5567 - accuracy: 0.7961 Epoch 14: val_accuracy did not improve from 0.80833 28/28 [==============================] - 10s 371ms/step - loss: 0.5567 - accuracy: 0.7961 - val_loss: 0.5195 - val_accuracy: 0.7950 Epoch 15/64 28/28 [==============================] - ETA: 0s - loss: 0.5142 - accuracy: 0.8130 Epoch 15: val_accuracy improved from 0.80833 to 0.83333, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 11s 411ms/step - loss: 0.5142 - accuracy: 0.8130 - val_loss: 0.4631 - val_accuracy: 0.8333 Epoch 16/64 28/28 [==============================] - ETA: 0s - loss: 0.5498 - accuracy: 0.8115 Epoch 16: val_accuracy did not improve from 0.83333 28/28 [==============================] - 11s 387ms/step - loss: 0.5498 - accuracy: 0.8115 - val_loss: 0.4813 - val_accuracy: 0.8300 Epoch 17/64 28/28 [==============================] - ETA: 0s - loss: 0.4881 - accuracy: 0.8195 Epoch 17: val_accuracy did not improve from 0.83333 28/28 [==============================] - 11s 391ms/step - loss: 0.4881 - accuracy: 0.8195 - val_loss: 0.4872 - val_accuracy: 0.8217 Epoch 18/64 28/28 [==============================] - ETA: 0s - loss: 0.4603 - accuracy: 0.8397 Epoch 18: val_accuracy did not improve from 0.83333 28/28 [==============================] - 11s 398ms/step - loss: 0.4603 - accuracy: 0.8397 - val_loss: 0.4960 - val_accuracy: 0.8100 Epoch 19/64 28/28 [==============================] - ETA: 0s - loss: 0.4806 - accuracy: 0.8280 Epoch 19: val_accuracy did not improve from 0.83333 28/28 [==============================] - 11s 384ms/step - loss: 0.4806 - accuracy: 0.8280 - val_loss: 0.5149 - val_accuracy: 0.8183 Epoch 20/64 28/28 [==============================] - ETA: 0s - loss: 0.4251 - accuracy: 0.8463 Epoch 20: val_accuracy improved from 0.83333 to 0.86000, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 426ms/step - loss: 0.4251 - accuracy: 0.8463 - val_loss: 0.3852 - val_accuracy: 0.8600 Epoch 21/64 28/28 [==============================] - ETA: 0s - loss: 0.4085 - accuracy: 0.8400 Epoch 21: val_accuracy did not improve from 0.86000 28/28 [==============================] - 11s 394ms/step - loss: 0.4085 - accuracy: 0.8400 - val_loss: 0.4046 - val_accuracy: 0.8600 Epoch 22/64 28/28 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8463 Epoch 22: val_accuracy improved from 0.86000 to 0.86333, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 417ms/step - loss: 0.4032 - accuracy: 0.8463 - val_loss: 0.3837 - val_accuracy: 0.8633 Epoch 23/64 28/28 [==============================] - ETA: 0s - loss: 0.3785 - accuracy: 0.8624 Epoch 23: val_accuracy improved from 0.86333 to 0.88167, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 414ms/step - loss: 0.3785 - accuracy: 0.8624 - val_loss: 0.3447 - val_accuracy: 0.8817 Epoch 24/64 28/28 [==============================] - ETA: 0s - loss: 0.3708 - accuracy: 0.8690 Epoch 24: val_accuracy did not improve from 0.88167 28/28 [==============================] - 10s 374ms/step - loss: 0.3708 - accuracy: 0.8690 - val_loss: 0.3363 - val_accuracy: 0.8817 Epoch 25/64 28/28 [==============================] - ETA: 0s - loss: 0.3848 - accuracy: 0.8576 Epoch 25: val_accuracy did not improve from 0.88167 28/28 [==============================] - 11s 382ms/step - loss: 0.3848 - accuracy: 0.8576 - val_loss: 0.3267 - val_accuracy: 0.8800 Epoch 26/64 28/28 [==============================] - ETA: 0s - loss: 0.3527 - accuracy: 0.8697 Epoch 26: val_accuracy did not improve from 0.88167 28/28 [==============================] - 11s 384ms/step - loss: 0.3527 - accuracy: 0.8697 - val_loss: 0.3511 - val_accuracy: 0.8700 Epoch 27/64 28/28 [==============================] - ETA: 0s - loss: 0.3730 - accuracy: 0.8664 Epoch 27: val_accuracy did not improve from 0.88167 28/28 [==============================] - 10s 370ms/step - loss: 0.3730 - accuracy: 0.8664 - val_loss: 0.3945 - val_accuracy: 0.8517 Epoch 28/64 28/28 [==============================] - ETA: 0s - loss: 0.3521 - accuracy: 0.8748 Epoch 28: val_accuracy did not improve from 0.88167 28/28 [==============================] - 11s 380ms/step - loss: 0.3521 - accuracy: 0.8748 - val_loss: 0.3644 - val_accuracy: 0.8783 Epoch 29/64 28/28 [==============================] - ETA: 0s - loss: 0.3419 - accuracy: 0.8770 Epoch 29: val_accuracy improved from 0.88167 to 0.89000, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 415ms/step - loss: 0.3419 - accuracy: 0.8770 - val_loss: 0.3061 - val_accuracy: 0.8900 Epoch 30/64 28/28 [==============================] - ETA: 0s - loss: 0.3269 - accuracy: 0.8843 Epoch 30: val_accuracy did not improve from 0.89000 28/28 [==============================] - 10s 371ms/step - loss: 0.3269 - accuracy: 0.8843 - val_loss: 0.3502 - val_accuracy: 0.8800 Epoch 31/64 28/28 [==============================] - ETA: 0s - loss: 0.3262 - accuracy: 0.8810 Epoch 31: val_accuracy did not improve from 0.89000 28/28 [==============================] - 11s 377ms/step - loss: 0.3262 - accuracy: 0.8810 - val_loss: 0.3315 - val_accuracy: 0.8817 Epoch 32/64 28/28 [==============================] - ETA: 0s - loss: 0.3037 - accuracy: 0.8957 Epoch 32: val_accuracy improved from 0.89000 to 0.89333, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 412ms/step - loss: 0.3037 - accuracy: 0.8957 - val_loss: 0.3035 - val_accuracy: 0.8933 Epoch 33/64 28/28 [==============================] - ETA: 0s - loss: 0.2977 - accuracy: 0.8902 Epoch 33: val_accuracy did not improve from 0.89333 28/28 [==============================] - 10s 372ms/step - loss: 0.2977 - accuracy: 0.8902 - val_loss: 0.2949 - val_accuracy: 0.8883 Epoch 34/64 28/28 [==============================] - ETA: 0s - loss: 0.3060 - accuracy: 0.8946 Epoch 34: val_accuracy did not improve from 0.89333 28/28 [==============================] - 11s 382ms/step - loss: 0.3060 - accuracy: 0.8946 - val_loss: 0.3280 - val_accuracy: 0.8867 Epoch 35/64 28/28 [==============================] - ETA: 0s - loss: 0.2917 - accuracy: 0.9034 Epoch 35: val_accuracy improved from 0.89333 to 0.90333, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 413ms/step - loss: 0.2917 - accuracy: 0.9034 - val_loss: 0.2756 - val_accuracy: 0.9033 Epoch 36/64 28/28 [==============================] - ETA: 0s - loss: 0.2866 - accuracy: 0.8997 Epoch 36: val_accuracy improved from 0.90333 to 0.91667, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 423ms/step - loss: 0.2866 - accuracy: 0.8997 - val_loss: 0.2602 - val_accuracy: 0.9167 Epoch 37/64 28/28 [==============================] - ETA: 0s - loss: 0.2854 - accuracy: 0.8971 Epoch 37: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 377ms/step - loss: 0.2854 - accuracy: 0.8971 - val_loss: 0.3099 - val_accuracy: 0.8867 Epoch 38/64 28/28 [==============================] - ETA: 0s - loss: 0.2817 - accuracy: 0.8996 Epoch 38: val_accuracy did not improve from 0.91667 28/28 [==============================] - 10s 373ms/step - loss: 0.2817 - accuracy: 0.8996 - val_loss: 0.2962 - val_accuracy: 0.8917 Epoch 39/64 28/28 [==============================] - ETA: 0s - loss: 0.2943 - accuracy: 0.8902 Epoch 39: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 375ms/step - loss: 0.2943 - accuracy: 0.8902 - val_loss: 0.2777 - val_accuracy: 0.9067 Epoch 40/64 28/28 [==============================] - ETA: 0s - loss: 0.2632 - accuracy: 0.9081 Epoch 40: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 380ms/step - loss: 0.2632 - accuracy: 0.9081 - val_loss: 0.2558 - val_accuracy: 0.9117 Epoch 41/64 28/28 [==============================] - ETA: 0s - loss: 0.2694 - accuracy: 0.9056 Epoch 41: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 394ms/step - loss: 0.2694 - accuracy: 0.9056 - val_loss: 0.3021 - val_accuracy: 0.8933 Epoch 42/64 28/28 [==============================] - ETA: 0s - loss: 0.2726 - accuracy: 0.9023 Epoch 42: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 381ms/step - loss: 0.2726 - accuracy: 0.9023 - val_loss: 0.2703 - val_accuracy: 0.9117 Epoch 43/64 28/28 [==============================] - ETA: 0s - loss: 0.2662 - accuracy: 0.8979 Epoch 43: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 380ms/step - loss: 0.2662 - accuracy: 0.8979 - val_loss: 0.2727 - val_accuracy: 0.9017 Epoch 44/64 28/28 [==============================] - ETA: 0s - loss: 0.2549 - accuracy: 0.9096 Epoch 44: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 380ms/step - loss: 0.2549 - accuracy: 0.9096 - val_loss: 0.3719 - val_accuracy: 0.8850 Epoch 45/64 28/28 [==============================] - ETA: 0s - loss: 0.2745 - accuracy: 0.9026 Epoch 45: val_accuracy did not improve from 0.91667 28/28 [==============================] - 10s 370ms/step - loss: 0.2745 - accuracy: 0.9026 - val_loss: 0.2903 - val_accuracy: 0.9050 Epoch 46/64 28/28 [==============================] - ETA: 0s - loss: 0.2295 - accuracy: 0.9184 Epoch 46: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 379ms/step - loss: 0.2295 - accuracy: 0.9184 - val_loss: 0.2614 - val_accuracy: 0.9067 Epoch 47/64 28/28 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9151 Epoch 47: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 381ms/step - loss: 0.2235 - accuracy: 0.9151 - val_loss: 0.2556 - val_accuracy: 0.9067 Epoch 48/64 28/28 [==============================] - ETA: 0s - loss: 0.2262 - accuracy: 0.9187 Epoch 48: val_accuracy did not improve from 0.91667 28/28 [==============================] - 10s 374ms/step - loss: 0.2262 - accuracy: 0.9187 - val_loss: 0.2536 - val_accuracy: 0.9150 Epoch 49/64 28/28 [==============================] - ETA: 0s - loss: 0.2191 - accuracy: 0.9173 Epoch 49: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 376ms/step - loss: 0.2191 - accuracy: 0.9173 - val_loss: 0.2559 - val_accuracy: 0.9167 Epoch 50/64 28/28 [==============================] - ETA: 0s - loss: 0.2098 - accuracy: 0.9246 Epoch 50: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 381ms/step - loss: 0.2098 - accuracy: 0.9246 - val_loss: 0.2556 - val_accuracy: 0.9133 Epoch 51/64 28/28 [==============================] - ETA: 0s - loss: 0.1975 - accuracy: 0.9239 Epoch 51: val_accuracy did not improve from 0.91667 28/28 [==============================] - 11s 378ms/step - loss: 0.1975 - accuracy: 0.9239 - val_loss: 0.2566 - val_accuracy: 0.9150 Epoch 52/64 28/28 [==============================] - ETA: 0s - loss: 0.2054 - accuracy: 0.9272 Epoch 52: val_accuracy improved from 0.91667 to 0.92000, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 412ms/step - loss: 0.2054 - accuracy: 0.9272 - val_loss: 0.2396 - val_accuracy: 0.9200 Epoch 53/64 28/28 [==============================] - ETA: 0s - loss: 0.2387 - accuracy: 0.9114 Epoch 53: val_accuracy did not improve from 0.92000 28/28 [==============================] - 11s 376ms/step - loss: 0.2387 - accuracy: 0.9114 - val_loss: 0.2456 - val_accuracy: 0.9183 Epoch 54/64 28/28 [==============================] - ETA: 0s - loss: 0.2033 - accuracy: 0.9242 Epoch 54: val_accuracy did not improve from 0.92000 28/28 [==============================] - 11s 385ms/step - loss: 0.2033 - accuracy: 0.9242 - val_loss: 0.2560 - val_accuracy: 0.9133 Epoch 55/64 28/28 [==============================] - ETA: 0s - loss: 0.1942 - accuracy: 0.9323 Epoch 55: val_accuracy did not improve from 0.92000 28/28 [==============================] - 10s 380ms/step - loss: 0.1942 - accuracy: 0.9323 - val_loss: 0.2748 - val_accuracy: 0.9133 Epoch 56/64 28/28 [==============================] - ETA: 0s - loss: 0.1995 - accuracy: 0.9264 Epoch 56: val_accuracy did not improve from 0.92000 28/28 [==============================] - 10s 366ms/step - loss: 0.1995 - accuracy: 0.9264 - val_loss: 0.3972 - val_accuracy: 0.8733 Epoch 57/64 28/28 [==============================] - ETA: 0s - loss: 0.2177 - accuracy: 0.9209 Epoch 57: val_accuracy did not improve from 0.92000 28/28 [==============================] - 11s 383ms/step - loss: 0.2177 - accuracy: 0.9209 - val_loss: 0.2530 - val_accuracy: 0.9150 Epoch 58/64 28/28 [==============================] - ETA: 0s - loss: 0.1948 - accuracy: 0.9308 Epoch 58: val_accuracy did not improve from 0.92000 28/28 [==============================] - 11s 381ms/step - loss: 0.1948 - accuracy: 0.9308 - val_loss: 0.2751 - val_accuracy: 0.9067 Epoch 59/64 28/28 [==============================] - ETA: 0s - loss: 0.1828 - accuracy: 0.9348 Epoch 59: val_accuracy improved from 0.92000 to 0.92833, saving model to /content/drive/Shareddrives/Computer Vision Final Project/CervicalCancer/cervical_cancer_best_model.hdf5 28/28 [==============================] - 12s 417ms/step - loss: 0.1828 - accuracy: 0.9348 - val_loss: 0.2280 - val_accuracy: 0.9283 Epoch 60/64 28/28 [==============================] - ETA: 0s - loss: 0.1699 - accuracy: 0.9385 Epoch 60: val_accuracy did not improve from 0.92833 28/28 [==============================] - 10s 364ms/step - loss: 0.1699 - accuracy: 0.9385 - val_loss: 0.2625 - val_accuracy: 0.9233 Epoch 61/64 28/28 [==============================] - ETA: 0s - loss: 0.1643 - accuracy: 0.9381 Epoch 61: val_accuracy did not improve from 0.92833 28/28 [==============================] - 10s 375ms/step - loss: 0.1643 - accuracy: 0.9381 - val_loss: 0.2678 - val_accuracy: 0.9183 Epoch 62/64 28/28 [==============================] - ETA: 0s - loss: 0.1835 - accuracy: 0.9268 Epoch 62: val_accuracy did not improve from 0.92833 28/28 [==============================] - 11s 378ms/step - loss: 0.1835 - accuracy: 0.9268 - val_loss: 0.2089 - val_accuracy: 0.9250 Epoch 63/64 28/28 [==============================] - ETA: 0s - loss: 0.1721 - accuracy: 0.9374 Epoch 63: val_accuracy did not improve from 0.92833 28/28 [==============================] - 11s 374ms/step - loss: 0.1721 - accuracy: 0.9374 - val_loss: 0.2228 - val_accuracy: 0.9250 Epoch 64/64 28/28 [==============================] - ETA: 0s - loss: 0.1624 - accuracy: 0.9374 Epoch 64: val_accuracy did not improve from 0.92833 28/28 [==============================] - 10s 372ms/step - loss: 0.1624 - accuracy: 0.9374 - val_loss: 0.2717 - val_accuracy: 0.9133
# Loading the Best Fit Model
model = load_model(root_dir + "/cervical_cancer_best_model.hdf5")
# Checking the Accuracy of the Model
accuracy = model.evaluate_generator(generator= test_data)[1]
print(f"The accuracy of your model is = {accuracy*100} %")
The accuracy of your model is = 92.77504086494446 %
# [1]: This accesses the second element of the returned list, which corresponds to the accuracy of the model. The first element ([0]) is the loss.
h = cnn.history;
h.keys();
# Ploting Accuracy In Training Set & Validation Set
plt.plot(h['accuracy'])
plt.plot(h['val_accuracy'] , c = "red")
plt.title("acc vs v-acc")
plt.show()
# Ploting Loss In Training Set & Validation Set
plt.plot(h['loss'])
plt.plot(h['val_loss'] , c = "red")
plt.title("loss vs v-loss")
plt.show()
def cancerPrediction(path):
classes_dir = ["Dyskeratotic","Koilocytotic","Metaplastic","Parabasal","Superficial-Intermediate"]
# Loading Image
img = image.load_img(path, target_size=(64,64))
# Normalizing Image
norm_img = image.img_to_array(img)/255
# Converting Image to Numpy Array
input_arr_img = np.array([norm_img])
# Getting Predictions
pred = np.argmax(model.predict(input_arr_img))
# Printing Model Prediction
print(classes_dir[pred])
path = "/content/drive/Shareddrives/Computer Vision Final Project/im_Dyskeratotic/im_Dyskeratotic/CROPPED/002_04.bmp"
cancerPrediction(path)
1/1 [==============================] - 0s 446ms/step Dyskeratotic
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix
# Get the true labels and predictions
y_true = test_data.classes
# Predict the probabilities for each class
y_pred = model.predict(test_data)
# Convert probabilities to class labels using np.argmax
y_pred_classes = np.argmax(y_pred, axis=1)
# Calculate the classification report
report = classification_report(y_true, y_pred_classes, target_names=test_data.class_indices.keys())
print("Classification Report:\n", report)
# Calculate and print the confusion matrix
cm = confusion_matrix(y_true, y_pred_classes)
print("Confusion Matrix:\n", cm)